{"doi":"10.3390/agronomy12040860","title":"Describing Lettuce Growth Using Morphological Features Combined with Nonlinear Models","abstract":"<jats:p>The aim of this study was to describe the sigmoidal growth behaviour of a lettuce canopy using three nonlinear models. Gompertz, Logistic and grey Verhulst growth models were established for the top projected canopy area (TPCA), top projected canopy perimeter (TPCP) and plant height (PH), which were measured by two machine vision views and 3D point clouds data. Satisfactory growth curve fitting was obtained using two evaluation criteria: the coefficient of determination (R2) and the mean absolute percentage error (MAPE). The grey Verhulst models produced a better fit for the growth of TPCA and TPCP, with higher R2 (RTPCA2=0.9097, RTPCP2=0.8536) and lower MAPE (MAPETPCA=0.0284, MAPETPCP=0.0794) values, whereas the Logistic model produced a better fit for changes in PH (RPH2=0.8991, MAPEPH=0.0344). The maximum growth rate point and the beginning and end points of the rapid growth stage were determined by calculating the second and third derivatives of the models, permitting a more detailed description of their sigmoidal behaviour. The initial growth stage was 1–5.5 days, and the rapid growth stage lasted from 5.6 to 26.2 days. After 26.3 days, lettuce entered the senescent stage. These inflections and critical points can be used to gain a better understanding of the growth behaviour of lettuce, thereby helping researchers or agricultural extension agents to promote growth, determine the optimal harvest period and plan commercial production.</jats:p>","journal":"Agronomy","year":2022,"id":47049,"datarank":1.0087405562100433,"base_score":3.258096538021482,"endowment":3.258096538021482,"self_citation_contribution":0.4887144807032224,"citation_network_contribution":0.520026075506821,"self_endowment_contribution":0.4887144807032224,"citer_contribution":0.520026075506821,"corpus_percentile":null,"corpus_rank":null,"citation_count":25,"citer_count":25,"citers_with_citation_signal":17,"citers_with_endowment":17,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":217322,"name":"Hongyan Gao","orcid":"0000-0003-2878-6618","position":1,"is_corresponding":false},{"id":118157,"name":"Xiaodong Zhang","orcid":"0000-0003-3260-870X","position":2,"is_corresponding":false},{"id":217323,"name":"Jiheng Ni","orcid":null,"position":3,"is_corresponding":false},{"id":217324,"name":"Hanping Mao","orcid":null,"position":4,"is_corresponding":false},{"id":217321,"name":"Qinglin Li","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Describing Lettuce Growth Using Morphological Features Combined with Nonlinear Models","abstract":"<jats:p>The aim of this study was to describe the sigmoidal growth behaviour of a lettuce canopy using three nonlinear models. Gompertz, Logistic and grey Verhulst growth models were established for the top projected canopy area (TPCA), top projected canopy perimeter (TPCP) and plant height (PH), which were measured by two machine vision views and 3D point clouds data. Satisfactory growth curve fitting was obtained using two evaluation criteria: the coefficient of determination (R2) and the mean absolute percentage error (MAPE). The grey Verhulst models produced a better fit for the growth of TPCA and TPCP, with higher R2 (RTPCA2=0.9097, RTPCP2=0.8536) and lower MAPE (MAPETPCA=0.0284, MAPETPCP=0.0794) values, whereas the Logistic model produced a better fit for changes in PH (RPH2=0.8991, MAPEPH=0.0344). The maximum growth rate point and the beginning and end points of the rapid growth stage were determined by calculating the second and third derivatives of the models, permitting a more detailed description of their sigmoidal behaviour. The initial growth stage was 1–5.5 days, and the rapid growth stage lasted from 5.6 to 26.2 days. After 26.3 days, lettuce entered the senescent stage. These inflections and critical points can be used to gain a better understanding of the growth behaviour of lettuce, thereby helping researchers or agricultural extension agents to promote growth, determine the optimal harvest period and plan commercial production.</jats:p>","is_dataset_classified":null,"base_score":3.258096538021482,"endowment":3.258096538021482,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19910364","pmcid":null,"openalex_id":"https://openalex.org/W4220694250","authors":[],"funders":[{"funder_name":"Project of Faculty of Agricultural Equipment of Jiangsu University","grant_id":"NZXB20200203","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"61771224","title":null},{"funder_name":"Open Fund of the Ministry of Education Key Laboratory of Modern Agricultural Equipment and Technology","grant_id":"JNZ201903","title":null},{"funder_name":"Hightech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province","grant_id":"MAET202111","title":null}],"total_grants":4,"fwci":4.2173,"citation_percentile":0.93429437,"influential_citations":0,"citation_trend":[{"year":2023,"count":6},{"year":2024,"count":7},{"year":2025,"count":8},{"year":2026,"count":4}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://www.mdpi.com/2073-4395/12/4/860/pdf?version=1650533102","host_type":"journal"},{"url":"https://www.mdpi.com/2073-4395/12/4/860/pdf?version=1650533102","host_type":"GOLD"},{"url":"https://www.mdpi.com/2073-4395/12/4/860/pdf?version=1650533102","host_type":"publisher"},{"url":"https://www.mdpi.com/2073-4395/12/4/860/pdf","host_type":"publisher"},{"url":"https://doi.org/10.3390/agronomy12040860","host_type":"journal"},{"url":"https://doaj.org/article/1fcc2be07bf14b12a0f06204fde890f2","host_type":"repository"},{"url":"https://dx.doi.org/10.3390/agronomy12040860","host_type":"repository"}],"fields_of_study":["Greenhouse Technology and Climate Control","Leaf Properties and Growth Measurement","Remote Sensing in Agriculture","Environmental Science","Agricultural and Food Sciences"],"mesh_terms":[],"keywords":["Gompertz function","Sigmoid function","Canopy","Mathematics","Growth curve (statistics)","Statistics","Stage (stratigraphy)","Growth model","Logistic function","Coefficient of determination","Growth rate","Computer science","Botany","Artificial neural network","Biology","Artificial intelligence","Geometry"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Zero hunger"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-16T00:09:32.356301Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}